Load packages
library(sf)
## Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE
library(rgdal)
## Loading required package: sp
## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
##
## rgdal: version: 1.5-32, (SVN revision 1176)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.4.3, released 2022/04/22
## Path to GDAL shared files: C:/Users/diana/AppData/Local/R/win-library/4.2/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/diana/AppData/Local/R/win-library/4.2/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.5-0
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(tmap)
library(tmaptools)
library(janitor)
##
## Attaching package: 'janitor'
##
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
Read dataset as df
df <- read_delim("HDR21-22_Composite_indices_complete_time_series.csv", delim=",")
## Rows: 206 Columns: 1008
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): iso3, country, hdicode, region
## dbl (1004): hdi_rank_2021, hdi_1990, hdi_1991, hdi_1992, hdi_1993, hdi_1994,...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Data cleaning and adding new Gender Inequality Index Difference
between 2010 & 2019 column (gii_diff)
gii <- df %>%
clean_names() %>%
select(iso3, country, gii_2010, gii_2019, gii_rank_2021) %>%
slice(1:195) %>%
mutate(., gii_diff=(gii_2019-gii_2010)) %>%
na.omit(df)
gii
## # A tibble: 165 × 6
## iso3 country gii_2010 gii_2019 gii_rank_2021 gii_diff
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 AFG Afghanistan 0.753 0.665 167 -0.088
## 2 AGO Angola 0.551 0.537 136 -0.0140
## 3 ALB Albania 0.246 0.156 39 -0.09
## 4 ARE United Arab Emirates 0.175 0.056 11 -0.119
## 5 ARG Argentina 0.364 0.306 69 -0.058
## 6 ARM Armenia 0.346 0.239 53 -0.107
## 7 AUS Australia 0.136 0.08 19 -0.056
## 8 AUT Austria 0.111 0.054 12 -0.057
## 9 AZE Azerbaijan 0.338 0.314 70 -0.0240
## 10 BDI Burundi 0.54 0.507 127 -0.0330
## # … with 155 more rows
Read world countries spatial data (shapefile) as world
world <- st_read("World_Countries_(Generalized)/World_Countries__Generalized_.shp")
## Reading layer `World_Countries__Generalized_' from data source
## `C:\Users\diana\CASA\Term1\0005_GIS\Practical\week5\wk5exam1\gis_hw4\World_Countries_(Generalized)\World_Countries__Generalized_.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 251 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS: WGS 84
Load the countrycode package and change the countrycode so that it
can be joined
library(countrycode)
gii$iso = countrycode(gii$iso3,"iso3c","iso2c")
Join the csv data and shapefile
world
## Simple feature collection with 251 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS: WGS 84
## First 10 features:
## FID COUNTRY ISO COUNTRYAFF AFF_ISO SHAPE_Leng
## 1 1 Afghanistan AF Afghanistan AF 50.8032097
## 2 2 Albania AL Albania AL 9.6250380
## 3 3 Algeria DZ Algeria DZ 70.5400822
## 4 4 American Samoa AS United States US 0.6001244
## 5 5 Andorra AD Andorra AD 0.9373518
## 6 6 Angola AO Angola AO 58.8734698
## 7 7 Anguilla AI United Kingdom GB 0.4683887
## 8 8 Antarctica AQ <NA> <NA> 1564.1441426
## 9 9 Antigua and Barbuda AG Antigua and Barbuda AG 1.1629014
## 10 10 Argentina AR Argentina AR 138.7513483
## SHAPE_Area geometry
## 1 6.256671e+01 MULTIPOLYGON (((61.27655 35...
## 2 3.073540e+00 MULTIPOLYGON (((19.57083 41...
## 3 2.135135e+02 MULTIPOLYGON (((4.603354 36...
## 4 1.371972e-02 MULTIPOLYGON (((-170.7439 -...
## 5 5.558517e-02 MULTIPOLYGON (((1.445836 42...
## 6 1.038131e+02 MULTIPOLYGON (((23.47611 -1...
## 7 7.797911e-03 MULTIPOLYGON (((-63.16778 1...
## 8 5.672928e+03 MULTIPOLYGON (((-180 -84.30...
## 9 4.562595e-02 MULTIPOLYGON (((-61.73806 1...
## 10 2.783090e+02 MULTIPOLYGON (((-71.85916 -...
joined_data <- world %>%
clean_names() %>%
left_join(.,
gii,
by = c("iso" = "iso"))
print(joined_data)
## Simple feature collection with 251 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -89 xmax: 180 ymax: 83.6236
## Geodetic CRS: WGS 84
## First 10 features:
## fid country.x iso countryaff aff_iso shape_leng
## 1 1 Afghanistan AF Afghanistan AF 50.8032097
## 2 2 Albania AL Albania AL 9.6250380
## 3 3 Algeria DZ Algeria DZ 70.5400822
## 4 4 American Samoa AS United States US 0.6001244
## 5 5 Andorra AD Andorra AD 0.9373518
## 6 6 Angola AO Angola AO 58.8734698
## 7 7 Anguilla AI United Kingdom GB 0.4683887
## 8 8 Antarctica AQ <NA> <NA> 1564.1441426
## 9 9 Antigua and Barbuda AG Antigua and Barbuda AG 1.1629014
## 10 10 Argentina AR Argentina AR 138.7513483
## shape_area iso3 country.y gii_2010 gii_2019 gii_rank_2021 gii_diff
## 1 6.256671e+01 AFG Afghanistan 0.753 0.665 167 -0.088
## 2 3.073540e+00 ALB Albania 0.246 0.156 39 -0.090
## 3 2.135135e+02 DZA Algeria 0.519 0.437 126 -0.082
## 4 1.371972e-02 <NA> <NA> NA NA NA NA
## 5 5.558517e-02 <NA> <NA> NA NA NA NA
## 6 1.038131e+02 AGO Angola 0.551 0.537 136 -0.014
## 7 7.797911e-03 <NA> <NA> NA NA NA NA
## 8 5.672928e+03 <NA> <NA> NA NA NA NA
## 9 4.562595e-02 <NA> <NA> NA NA NA NA
## 10 2.783090e+02 ARG Argentina 0.364 0.306 69 -0.058
## geometry
## 1 MULTIPOLYGON (((61.27655 35...
## 2 MULTIPOLYGON (((19.57083 41...
## 3 MULTIPOLYGON (((4.603354 36...
## 4 MULTIPOLYGON (((-170.7439 -...
## 5 MULTIPOLYGON (((1.445836 42...
## 6 MULTIPOLYGON (((23.47611 -1...
## 7 MULTIPOLYGON (((-63.16778 1...
## 8 MULTIPOLYGON (((-180 -84.30...
## 9 MULTIPOLYGON (((-61.73806 1...
## 10 MULTIPOLYGON (((-71.85916 -...
Plot the Gender Inequality Difference 2010 & 2019 map
tm_shape(joined_data) +
tm_polygons("gii_diff",
style="pretty",
palette="Blues",
midpoint=NA,
#title="Gender Inequality Difference 2010 & 2019",
alpha = 0.7) +
tm_compass(position = c("left", "bottom"),type = "arrow") +
tm_scale_bar(position = c("left", "bottom")) +
tm_layout(title = "Gender Inequality Difference 2010 & 2019", legend.position = c("right", "bottom")) +
tmap_options(max.categories = 115)
## Scale bar set for latitude km and will be different at the top and bottom of the map.
Plot the Gender Inequality Difference 2010 & 2019 map_for
wk4Exam
tmap_mode("plot")
## tmap mode set to plotting
breaks=c(-0.6,-0.03,-0.01, 0.01, 1, 50, 100, 150, 200)
tm1 <- tm_shape(joined_data) +
tm_polygons("gii_2019",
breaks=breaks,
palette="PuOr")+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)+
tm_credits("(a1: gii_2019)", position=c(0,0.85), size=1.5)
tm2 <- tm_shape(joined_data) +
tm_polygons("gii_2010",
breaks=breaks,
palette="PuOr")+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)+
tm_credits("(a2: gii_2010)", position=c(0,0.85), size=1.5)
tm3 <- tm_shape(joined_data) +
tm_polygons("gii_rank_2021",
breaks=breaks,
palette="PuOr")+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)+
tm_credits("(a3: gii_rank_2021)", position=c(0,0.85), size=1.5)
legend <- tm_shape(joined_data) +
tm_polygons("gii_diff",
breaks=breaks,midpoint = NA,
palette="PuOr") +
tm_scale_bar(position=c(-0.4,0.8), text.size=0.6)+
tm_compass(north=0, position=c(0.65,0.6))+
tm_layout(legend.only = TRUE, legend.position=c(0.2,0.25),asp=0.1)+
tm_credits("Gender Inequality Difference 2010 & 2019", position=c(0.0,0.0))
t=tmap_arrange(tm1, tm2, tm3, legend, ncol=2)
print(t)
## Scale bar set for latitude km and will be different at the top and bottom of the map.
library(grid)
grid.newpage()
pushViewport(viewport(layout=grid.layout(2,2)))
print(tm1, vp=viewport(layout.pos.col=2, layout.pos.row=2, height=7))
print(tm2, vp=viewport(layout.pos.col=1, layout.pos.row=2, height=7))
print(tm3, vp=viewport(layout.pos.col=2, layout.pos.row=1, height=7))
print(legend, vp=viewport(layout.pos.col=1, layout.pos.row=1, height=7))
## Scale bar set for latitude km and will be different at the top and bottom of the map.
tmap_mode("view")
## tmap mode set to interactive viewing
breaks=c(0,10,20,30,50,60,70,80,90,100,110,120,130,140,150,160)
tm_shape(joined_data) +
tm_polygons("gii_rank_2021", breaks=breaks)
## Warning: Values have found that are higher than the highest break